Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Watch and learn: optimizing from revealed preferences feedback

Watch and learn: optimizing from revealed preferences feedback Watch and Learn: Optimizing from Revealed Preferences Feedback AARON ROTH University of Pennsylvania and JONATHAN ULLMAN Northeastern University and ZHIWEI STEVEN WU University of Pennsylvania A Stackelberg game is played between a leader and a follower. The leader first chooses an action, and then the follower plays his best response, and the goal of the leader is to pick the action that will maximize his payoff given the follower's best response. Stackelberg games capture, for example, the following interaction between a retailer and a buyer. The retailer chooses the prices of the goods he produces, and then the buyer chooses to buy a utility-maximizing bundle of goods. The goal of the retailer here is to set prices to maximize his profit--his revenue minus the production cost of the purchased bundle. It is quite natural that the retailer in this example would not know the buyer's utility function. However, he does have access to revealed preference feedback--he can set prices, and then observe the purchased bundle and his own profit. We give algorithms for efficiently solving, in terms of both computational and query complexity, a broad class of Stackelberg games in which the follower's utility function is unknown, using http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Watch and learn: optimizing from revealed preferences feedback

Download PDF
4 pages

Loading next page...
 
/lp/association-for-computing-machinery/watch-and-learn-optimizing-from-revealed-preferences-feedback-0adfnF4Oep

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/2845926.2845934
Publisher site
See Article on Publisher Site

Abstract

Watch and Learn: Optimizing from Revealed Preferences Feedback AARON ROTH University of Pennsylvania and JONATHAN ULLMAN Northeastern University and ZHIWEI STEVEN WU University of Pennsylvania A Stackelberg game is played between a leader and a follower. The leader first chooses an action, and then the follower plays his best response, and the goal of the leader is to pick the action that will maximize his payoff given the follower's best response. Stackelberg games capture, for example, the following interaction between a retailer and a buyer. The retailer chooses the prices of the goods he produces, and then the buyer chooses to buy a utility-maximizing bundle of goods. The goal of the retailer here is to set prices to maximize his profit--his revenue minus the production cost of the purchased bundle. It is quite natural that the retailer in this example would not know the buyer's utility function. However, he does have access to revealed preference feedback--he can set prices, and then observe the purchased bundle and his own profit. We give algorithms for efficiently solving, in terms of both computational and query complexity, a broad class of Stackelberg games in which the follower's utility function is unknown, using

Journal

ACM SIGecom Exchanges – Association for Computing Machinery

Published: Nov 12, 2015

There are no references for this article.